This dataset represents the Integrated biodiversity status assessment for benthic habitats using the BEAT tool. Status is shown in five categories based on the integrated assessment scores obtained in the tool. Biological Quality Ratios (BQR) above 0.6 correspond to good status. The assessment in open sea areas was based on the core indicators ‘State of the soft-bottom macrofauna community’ and ‘Oxygen debt’. Coastal areas were assessed by national indicators, and may hence not be directly comparable with each other. This dataset displays the result of the integrated biodiverity status in HELCOM Assessment unit Scale 4 (Division of the Baltic Sea into 17 sub-basins and further division into coastal and off-shore areas and division of the coastal areas by WFD water types or water bodies). Attribute information: "BQR" = Biological Quality Ratio "Confidence" = Confidence of the assessment "HELCOM_ID" = id of the HELCOM assessment unit "country" = name of the country / opensea "level_2" = HELCOM sub-basins (name of the scale 2 assessment unit) "Name" = Name of the coastal assessment unit on scale 4 "AULEVEL" = scale of the assessment units "type_descr" = Name of the HELCOM scale 4 assessment unit "SAUID" = ID number for the spatial assessment unit "EcosystemC" = Ecosystem component assessed "Confiden_1" = Confidence of the assessment (0-1, higher values mean higher confidence) "Total_numb" = Number of indicators used in assessment "Area_km2" = Area of assessment unit (km2) "Confiden_1" = Confidence level of the assessment (scores < 0.5 = low, 0.5 - 0.75 = intermediate, > 0.75 = high) "STATUS" = Integrated status category (0-0.2 = not good (lowest score), 0.2-0.4 = not good (lower score), 0.4-0.6 = not good (low score), 0.6-0.8 = good (high score), 0.8-1.0 = good (highest score))

Observations of Amauropsis islandica were collected from the Baltic Sea area for HELCOM Red List species list. The HELCOM Red List of Baltic Sea species in danger of becoming extinct (2013) is the first threat assessment for Baltic Sea species that covers all marine mammals, fish, birds, macrophytes (aquatic plants), and benthic invertebrates, and follows the Red List criteria of the International Union for Conservation of Nature (IUCN). Almost 2800 species were considered in the Red List assessment and about 1750 were evaluated according to the IUCN Red List criteria. Amauropsis islandica has been placed to the Red List category of Near Threatened (NT) species. Dataset for download contains spatial grid of the Baltic Sea. Distribution of the species can be found in corresponding name column. Values are coded: 1 - Present before year 2000 or in 2000, 2 - Present after year 2000, 3 - Present both before and after year 2000.

The assessment results for pelagic habitats for the HELCOM Third Holistic Assessment of the Ecosystem Health of the Baltic Sea (HOLAS 3) based on integrating following indicators: Zooplankton mean size and total stock, Seasonal succession of dominating phytoplankton groups, Cyanobacterial bloom index, Diatom-dinoflagellate index. Status is shown in five categories based on the integrated assessment scores obtained in the tool. Biological Quality Ratios (BQR) above 0.6 correspond to good status. The datasets were created using the BEAT tool. https://github.com/helcomsecretariat/BEAT German coastal areas were assessed based on WFD indicators.

This dataset represents the underlying data on core indicator Zooplankton mean size and total stock (MSTS). The core indicator evaluates zooplankton community structure to determine whether it reflects good environmental status. Quantitative thresholds are used to evaluate core indicators status in Achieve, Fail or Not assessed. As a rule, achieve status occur when large sized zooplankters occur abundantly in the plankton community. Due to strong environmental gradients, size distribution and total stock of the zooplankton corresponding to achieve status vary between the Baltic Sea sub-basins. This dataset displays the result of the indicator in HELCOM Assessment Scale 2 (Division of the Baltic Sea into 17 sub-basins). The dataset contains following attributes: "HELCOM_ID" = ID of the HELCOM scale 2 assessment unit "SB_Code" = Code of the HELCOM subbasin "Name" = Name of the HELCOM scale 2 assessment unit "Threshold value" = Threshold value for this indicator "Indicator value" = Value for the indicator in HELCOM scale 2 assessment unit "Status" = Status of the indicator (“Good”, “Not good” or “Not assessed”) "Info" = additional information "AULEVEL" = Assessment unit level used for the indicator "ConfA" = confidence of classification "ConfT" = Temporal confidence of classification "ConfS" = Spatial confidence of classification "ConfM" = Methodological confidence of classification

This dataset contains all indicator and assessment results and data for the State of the Baltic Sea (HOLAS II) 2017 version. Final 2018 versions are included separately in HELCOM Map and Data service and metadata catalogue. All datasets for HOLAS II and associated metadata were here compiled. The datasets are: Abundance of coastal fish key functional group cyprinids Abundance of coastal fish key functional group piscivores Abundance of coastal fish species Abundance of salmon spawners and smolt Abundance of waterbirds in the breeding season Abundance of waterbirds in the wintering season Chlorophyll a status Cyanobacterial bloom Dissolved inorganic nitrogen Dissolved inorganic phosphorous Distribution of grey seals Distribution of harbour seals Distribution of ringed seals Eutrophication assessment insitu data Heavy metals – Cadmium (Cd) levels Heavy metals – Lead (Pb) levels Heavy metals – Mercury (Hg) levels Hexabromocyclododecane (HBCDD) levels Integrated biodiversity status assessment for benthic habitats Integrated biodiversity status assessment for fish Integrated biodiversity status assessment for grey seals Integrated biodiversity status assessment for harbour porpoises Integrated biodiversity status assessment for pelagichabitats Integrated biodiversity status assessment for ringed seals Integrated biodiversity status assessment for seals Integrated contamination status assessment Integrated eutrophication status assessment Oxygen det PCB dioxin and furan Perfluorooctane sulphonate (PFOS) Polyaromatic hydrocarbons (PAH) and their metabolites Polybrominated diphenyl ethers Cesium-137 in fish Cesium-137 in flatfish Cesium-137 in herring Cesium-137 in seawater Malformed embryos of amphipods State of the soft bottom macrofauna community TBT and imposex Total nitrogen Total phosphorous Trend in arrival of new non-indigenous species Water clarity Zooplankton mean size and total stock MSTS

The data set is showing areas where estuaries occurs in the Baltic Sea area as polygon regions used for the BRISK project (Sub-regional risk of spill of oil and hazardous substances in the Baltic Sea, http://www.brisk.helcom.fi/). This dataset has been produced by COWI (http://www.cowi.dk) based on data collected from HELCOM, Finland (Copyright: SYKE), Lithuania, Russia and Sweden. The dataset includes data provided by the BRISK Project Partner organisations from various Baltic Sea countries. The detailed documentation of what partner provided what data is given in the Annex of the document: 70618-3.1.2.2 Data Collection Report. Estuaries have a high biodiversity. They are important breeding and foraging areas for many birds and reproduction areas for many fish species. Stranded oil may degrade slowly and the risk for damage to the habitat is high during clean up actions. The organisms encountered in the habitat are generally very vulnerable to oil.

Potential cumulative impacts on benthic habitats is based on the same method than http://metadata.helcom.fi/geonetwork/srv/eng/catalog.search#/metadata/9477be37-94a9-4201-824a-f079bc27d097, but is focused on physical pressures and benthic habitats. The dataset was created based on separate analysis for potential cumulative impacts on only the benthic habitats, as these are particularly affected by physical pressures. In this case the evaluation was based on pressure layers representing http://metadata.helcom.fi/geonetwork/srv/eng/catalog.search#/metadata/ea0ef0fa-0517-40a9-866a-ce22b8948c88 and http://metadata.helcom.fi/geonetwork/srv/eng/catalog.search#/metadata/05e325f3-bc30-44a0-8f0b-995464011c82, combined with information on the distribution of eight broad benthic habitat types and five habitat-forming species (http://metadata.helcom.fi/geonetwork/srv/eng/catalog.search#/metadata/363cb353-46da-43f4-9906-7324738fe2c3, http://metadata.helcom.fi/geonetwork/srv/eng/catalog.search#/metadata/f9cc7b2c-4080-4b19-8c38-cac87955cb91, http://metadata.helcom.fi/geonetwork/srv/eng/catalog.search#/metadata/264ed572-403c-43bd-9707-345de8b9503c, http://metadata.helcom.fi/geonetwork/srv/eng/catalog.search#/metadata/822ddece-d96a-4036-9ad8-c4b599776eca and http://metadata.helcom.fi/geonetwork/srv/eng/catalog.search#/metadata/ca327bb1-d3cb-46c2-8316-f5f62f889090). The potential cumulative impacts has been estimated based on currently best available data, but spatial and temporal gaps may occur in underlying datasets. Please scroll down to "Lineage" and visit http://stateofthebalticsea.helcom.fi/cumulative-impacts/ for more info.

The dataset contains information on commercial fishery with unspecified gears in Baltic Sea in 2007 (Lithuanian data from 2008). Total catches/landings are given per ICES rectangle in tons. The dataset contains information on commercial fishery with unspecified gears in Baltic Sea in 2007 (Lithuanian data from 2008). Total catches/landings are given per ICES rectangle in tons. Unspecified gears (gear category 0) are gears that the countries have not defined more specifically. The total catches/landings with this type of gears is 150 tons. The catches/landings per ICES rectangle are also given per species. Data was requested from HELCOM contracting parties through HELCOM HOLAS project. Landings/ catches data from Finland, Sweden, Denmark, Germany, Poland, Lithuania, Latvia and Estonia is based on national data, while Russian data is based on the ICES report "Report of the Baltic Fisheries Assessment Working Group (WGBFAS)" (2008). Commercial fishery data was reported by the following persons/authorities to HELCOM Secretariat: Germany:Dr. Christopher Zimmermann (Institut für Ostseefischerei (OSF), Johann Heinrich von Thünen-Institut (vTI), Bundesforschungsinstitut für Ländliche Räume, Wald und Fischerei) Lithuania: Open sea: Irina Jakovleva (Fisheries Reguliation Division, Baltic Sea Fisheries Department, Fisheries Department under the Ministry of Agriculture); coastal: Rimantas Repecka (Head of Laboratory of Marine Ecology, Institute of Ecology of Vilnius University) Sweden: Jarl Engquist (Avdelningen för fiskerikontroll, Swedish Board of Fisheries) Latvia: Marina Fettere (Fishery and Information section, Latvian Fish Resources Agency) Finland: Pirkko Söderkultalahti (Finnish Game and Fisheries Research Institute) Estonia: Ministry of Agriculture Denmark: Troels Pade (The Danish Directorate of Fisheries, Ministry for Food, Agriculture and Fisheries) Russia: no contact person, data from ICES reports Poland: Malgorzata Marciniewicz-Mykieta (Departament Monitoringu i Informacji o Srodowisku, Glówny Inspektorat Ochrony Srodowiska) Abbreviation of species ABK=White Bream; ACC=Ruffe; ACH=Charr; ALR=Bleak; ANE=European anchovy; ANF=Anglerfishes; BLL=Brill; CAT=Rockfishes; CGO=Goldfish; COD=Cod; CRA=Marine crabs; CRE=Edible Crab; CSH=Common shrimp; DAB=Dab; DGS=Sourdog/Spint Dogfish; ELE=Eel; ELP=Eelpout; FAS=Asp; FBM=Bream; FBR=Freshwater breams; FBU=Burbot; FCC=Crucian carp; FCP=Common carp; FGR=Grayling; FID=Orfe (=Ide); FIE=Common dace; FLE=Flounder; FLX=Flatfishes; FPE=Perch; FPI=Pike; FPP=Zander (Pikeperch); FRD=Rudd; FRF=Freshwater fishes; FRO=Roach; FRU=Ruffe; FSB=Silver bream; FTE=Tench; FVE=Vendace (Cisco); FVI=Freshwater Vimba; GAR=Garfish; GPA=Gobies; GTA=Three-Spined Stickleback; GUG=Grey Gurnard; HAD=Haddock; HAL=Halibut; HER=Herring; HKE=Hake; HOM=Horse Mackarel; JAX=Jack and horse mackerels; LAR=River lamprey; LAU=Sea lamprey; LBE=European Lobster; LEM=Lemon Sole; LIN=Ling; LUM=Lumpfish; MAC=Mackerel; MLR=Thicklip Grey Mullet; MON=Monkfish; MQS=Four Spined Sculpin; MTL=Thick-lipped Mullet; MUL=Grey mullets; MUS=Blue mussel; MUX=Surmullets (= Red mullets); MZZ=Marine fishes; NBU=Round Goby NEP=Norway lobster; NOP=Norway pout; OTH=Other sp; PLA=Long-rough dab; PLE=Plaice; PLN=European Whitefish; POK=Saithe; POL=Pollack; PPZ=Common Prawn; PRA=Northern Pink Shrimp; SAL=Salmon; SAN=Sandeels; SKA=Skates, rays; SKB=Sticklebacks; SME=Smelt; SOL=Sole; SPR=Sprat; SQU=Squids; SRA=Atlantic searobins; SRE=Rudd; TAS=Twaite Shad; TGQ=Fourhorn Sculpin; TRB=Rainbow Trout; TRO=Trouts; TRR=Rainbow Trout; TRS=SeaTrout; TSD=Twaite Shad; TUR=Turbot; UNK=Unknown Catches; USK=Tusk; WEG=Greater Weever; WHB=Blue Whiting; WHF=Whitefish, Houting, Powan; WHG=Whiting; WIT=Witch; VIV=Vimba bream.

This dataset contains core indicator results developed under the HELCOM Third Holistic Assessment of the Ecosystem Health of the Baltic Sea (HOLAS 3).

The map of sprat relative abundance is mainly based on Baltic International acoustic surveys (BIAS), years 2011-2016, (ICES WGBIFS reports), reported as millions of sprat per ICES rectangle. The BIAS surveys cover almost the whole area where sprat is commonly encountered. Outside BIAS area, sprat landings data was used to complement the data. For ICES rectangles surveyed by BIAS, values shown are the mean values per ICES rectangle based on BIAS data, average for 2011-2016. For ICES rectangles not surveyed by BIAS, values are calculated as: MAX-value x Weighting factor. The weighting factor is specific to each ICES rectangle, calculated as the ratio between the commercial landings in that rectangle and the commercial landings in the ICES rectangle with highest landings (based on averages for 2011-2015). MAX-value = millions of sprat according to BIAS in the ICES rectangle with highest landings. ICES rectangles outside the BIAS survey area with no reported sprat landings were given the value 0. The abundance values / ICES rectangle were divided by the area of the rectangle to obtain values per 1km2, and then converted to 1 km x 1km grid cells. Values were first log transformed and then normalised.